1. Field of the Invention
The present invention relates to road detection and object detection, more particularly relates to a method and a device for detecting at least one road line as well as a method and a device for detecting at least one road region (hereinafter, sometimes called a “road surface region”).
2. Description of the Related Art
A driving assistance system has been widely used up to now. A lane/road detection warning (LDW/RDW) system is a sub-system of the driving assistance system; by using the LDW/RDW system, for example, it is possible to avoid a car crash, and to more accurately determine a driving direction. The lane/road detection is very important to the LDW/RDW system; as long as the road information is known, it is possible to carry out a further process, for example, communicating a warning message. In general, the lane/road detection is conducted by detecting a road line that may be a road shoulder made of stone, a white line, a fence, etc.
Due to environments and geographical features, many road surfaces are not flat, i.e., they slope upward or downward. For example, Chongqing of China has many uneven mountain road surfaces; Hong Kong of China, Tokyo of Japan, etc., are the same.
In some conventional leading methods on the basis of stereoscopic vision, a road surface is assumed to be flat; as a result, regarding a sloped road surface, it is impossible to accurately detect at least one road line therein.
In US Patent Application Publication No. 2006/0239509 A1, a road line recognition apparatus is disclosed that includes an imaging section, an image processing section, and a detection section. The detection section includes a road line candidate point detection and conversion processing unit for detecting a pixel on a road surface as a road line candidate point on the basis of luminance and a distance with regard to an image on one side, and performing Hough conversion of the road line candidate point. However, since this apparatus adopts the luminance to seek the pixel on the road surface, it may be relatively sensitive to the luminance.
In a paper titled “Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry Through V-disparity Representaion” (Raphael Labayrade, Didier Aubert, and Jean-Phisppe Tarel; IEEE Intelligent Vehicles Symposium 2002, pp 646-651, Vol. 2), a method of obtaining k lines having the highest Hough transform value from a V-disparity image is disclosed. Among the k lines, one having the highest cumulative total gray level is selected for road mapping; then a road surface region of interest may be acquired. After that, a road line may be sought from the road surface region of interest by employing the Hough transform.
In U.S. Pat. No. 7,346,190 B2, a traffic line recognition device is disclosed that includes a traffic line region candidate extraction section for extracting a traffic line candidate as a traffic line region candidate; a two dimension/three dimension transform section for transforming two dimensional image coordinates of a pixel contained in the traffic line region candidate into three dimensional coordinates; a histogram production section for accumulating and projecting the three dimensional coordinates onto a coordinate system in a direction crossing a road and producing a histogram in the direction crossing the road; and a traffic line judgment unit for determining a traffic line based on that histogram. However, this device may not be applied to a case where a road surface is uneven, for example, a case where there is a sloped road surface.
In addition, most of the conventional road detection methods are on the basis of a single color image or a polarization image. Therefore, as for an image in which road edges are dark, or a complex case, for example, a case in which there is a sloped road surface, the road detection may not have a good effect.